An Evapotranspiration Assimilation Method Based on Ensemble Kalman Filter and À Trous Wavelet

Shaohui Chen,Xiaomin Sun,Jing Tian,Xiangzheng Deng,Jianwei Qi

doi:10.1155/2013/531810

Abstract

It is challenging to assimilate the evapotranspiration product (EP) retrieved from satellite data into land surface models (LSMs). In this paper, a perturbed ensemble Kalman filter (PEKF) and à trous wavelet transform (AWT) integrated method are proposed to implement the evapotranspiration assimilation. In this method, the AWT is used to decompose the EPs into multiple channels since it is very powerful in fusing high frequency spatial information of multisource data, and then the Kalman filter is performed in the AWT domain. The proposed method combines the advantages of the PEKF that is capable of accommodating model error and observation error, and the AWT can effectively perform multiresolution fusion. Assimilation experiment conducted with the Noah model and the EP retrieved from the MODIS data shows that the proposed method performs better than the traditional ensemble Kalman filter (EnKF) and PEKF methods. The analysis results fit well with the evapotranspiration observation at two field sites with different land surface conditions. These indicate that the proposed method is promising for assimilating regional scale satellite retrieved EP into LSMs.

Highlights

Evapotranspiration (ET) is an important component of the water and energy exchanges between the atmosphere and land surface
We study the hybrid use of the atrous wavelet transform (AWT) and perturbed ensemble Kalman filter (PEKF) methods for assimilating the MODIS dataretrieved EP (MDRE) into the ET production (EP) of the Noah model in order to improve the consecutive simulation of the Noah model
The AWT is used to decompose the MDRE for injecting its detail information represented by wavelet planes, while the PEKF is used to complete the assimilation by the model and observation uncertainties

Summary

Introduction

Evapotranspiration (ET) is an important component of the water and energy exchanges between the atmosphere and land surface. Good quality of spatial and temporal ET production (EP) can help to improve comprehension of water and energy cycle. This kind of EP is generally difficult to obtain in both dimensions of space and time because ET is influenced by many factors, such as air and skin temperatures, soil moisture, vegetation fraction, and horizontal advection. One is site observations or remote sensing retrievals. Remote sensing retrievals have high spatial resolutions and can cover large range, but can only retrieve the instantaneous EP. LSMs are probably the most efficient approach for continuously estimating ET on a large range [1]. Data assimilation (DA) has been applied to integrate observational ET into LSMs [2]

Results

Discussion

Conclusion